1. Technical Field
The invention relates generally to control devices for controlling industrial or similar processes and particularly to self-tuning controllers. More particularly, the invention concerns feedforward controllers wherein operating parameters are developed in response to changes in the process inputs.
2. Background Art
Control systems regulate many material, energy and guidance systems. The home heat control thermostat is a familiar example. More sophisticated devices are used in refineries, and complex industrial facilities, where input materials are processed into products. Feedback and feedforward mechanisms are used to assure accurate production despite variations in the inputs, or changes in the desired output. Feedback control is the more common control method. Feedback compares the system output, called the controlled variable, with the desired output, called the set point, to generate an error value. The size and direction of error are used to correct the system response towards the desired output. Since feedback response to a load disturbance occurs after a change in the controlled variable has been sensed, the system is frequently not exactly on target. Mistuning by operators and changed system characteristics are additional sources for system error.
Recently self-tuning or adaptive feedback control systems have been developed that use abstract rules to reset the tuning of a control system as the device learns from its experience with the process. An adaptive system adjusts its control parameters as the process changes. Adaptive feedback controllers can be connected, turned on, and left to tune and retune themselves to provide consistently better control than manually adjusted controllers. As a result adaptive feedback controllers are increasingly used in industry.
Feedforward control is a rarer and more specialized control method. Feedforward recognizes that upsets in the inputs to the system can be used to adjust the system devices in anticipation of or simultaneously with the arrival of those upsets. If all the load variables for a particular process are sensed, transmitted and responded to without error, and if the relationship between manipulated and measured variables is exactly known, then perfect control is theoretically possible provided the ideal feedforward controller is stable and physically realizable.
Feedforward control is usually accomplished in one of two ways. One requires finding a process equation describing the controlled variable as a function of the load variables, manipulated variables and set point. Several relationships are used to balance the input and output material and energy in conjunction with other inferential or empirical relationships. The process equations provide a basis for a general transfer function relating the inputs to process results. Once the transfer function is formulated, load signals are processed by a compensator to generate a control action that preadjusts the manipulated variable in anticipation of the controlled variable's response to the load variations. Implementation of traditional feedforward then requires specific engineering of the particular process equations.
A second feedforward method presumes some generalized model for which coefficients of model terms are adjusted according to actual conditions. The generalized model is then specialized according to empirical methods. A generalized description usually requires complex mathematical processing which is exacerbated as the number of terms increase with the loads sensed. Such complexity requires more computing time and equipment for a specifically defined process. Since the generalized model is not restrained like a standard process equation is, for example, by a material energy balance equation, inaccuracies in the model must be specifically corrected. Practical implementation of generalized models then often leads to slow or inadequately performing devices.
Adaptive control techniques also make presumptions about the load and process to establish descriptive equations directed at exact models. In the immediate control procedure, time sampled data is applied continuously to the model to generate a control signal for the manipulated variable. For adaptation, the control model is continuously updated with each sample according to error evaluation procedures. The sample data driving the model is then concerned with the immediate present, the current values, and current rates of change, and how these cause an error between the model and the process. Unfortunately the narrow time frame of continuous models necessarily leads to the processing of the measurement noise along with any true system changes. Overly rapid adaptation to measurement noise in a feedforward system leads to inaccurate and erratic feedforward performance while failure to adapt quickly enough prevents timely adjustment of the feedforward controller allowing it to keep pace with a changing process.
Typically, load and manipulated variables enter the process at different locations and have process effects that are spread over time. Processes usually respond slowly, integrating load variations over time. The process response is then a time smearing of the inputs, making relations between the inputs and outputs complex. As a result data snapshots and static models are blind to the history of the process without memory of previous events. However, a broad time frame can average real information into insignificance. A problem then exists with the degree of influence prior events should have.
One method to correct feedforward control errors is to join a feedback controller with the feedforward device. Cumulative errors in the feedforward control cause the controlled variable to be offset from the set point. The feedback controller corrects the error, forcing the controlled variable to the set point. Feedback control corrects the feedforward offset error, but in doing so may disguise system response making proper feedforward adaptation difficult. Coordination between the feedforward and feedback actions can then be a problem, especially in an adaptive system.
Other problems have inhibited feedforward control deployment, especially in a multivariable adaptive form. Measurements may be inaccurate. Sensing all the loads may be impossible or excessively expensive. Models are never perfect in practice. Model relations may be unsolvable in an exact form, or time varying due to equipment wear. Model complexity may slow the computation, thereby limiting response time, or the number of loads controllable. Computational errors may occur or accumulate over time. Computational resources proportional to the square of the number of loads sensed may be required. The device may be ignorant of events having variable or unknown time duration. The device may not adapt correctly to all data particularly when several loads have related response results. The device may behave erratically with incomplete data. The device may not coordinate well with feedback controls and may act in conflict. Human intervention may be required to tune and retune the device.
It is believed few or no other practical general purpose adaptive feedforward controllers presently exist.
It is therefore an objective of the present invention to provide a robust adaptive feedforward controller.
It is an objective to provide a device wherein variations in the system relations, and variations in the timing attributes are accommodated by the adaptive feedforward control while at the same time accommodating load variations.
It is an objective to provide a control device that operates with incomplete data.
It is an objective to provide an adaptive controller for a number of disturbance variables whose rate of adaptation is reasonably quick with few disturbances on only subsets of the inputs.